摘要
为了避免传统的基于振动信号的内燃机主轴承磨损故障诊断中安装传感器以及提取故障特征频率的麻烦,提出了一种基于内燃机工作声信号小波包图像处理的方法.通过该方法,可以得到含有内燃机主轴承间隙磨损状态的时频信息,定义出各个标准故障状态的时-频分布图,建立了基于图像匹配技术的内燃机主轴承磨损故障诊断模型.通过比较待诊断时频分布图与所有故障模式时频分布图的欧氏距离,可以判断出轴承的间隙磨损状态.结果表明此方法简单有效、状态信息利用充分.
In order to avoid the difficulty of installing vibration sensors and extracting characteristic frequency vectors for the traditional vibration-based abrasion fault diagnosis on the main bearing of diesel engine, this paper presents a new approach based on wavelet packet images processing of sound signal of diesel engine. Thus, the standard time-frequency distribution images of all fault conditions including the gap abrasion information of the main bearing can be defined. Correspondingly, a gap abrasion fault diagnosis model of the main bearing with images matching is set up. Through comparing the Euclid distance between standard fault image and the test image, the model can recognize the gap abrasion condition. The results show that this method makes the best use of fault information and is simple and effective.
出处
《自动化学报》
EI
CSCD
北大核心
2004年第4期554-559,共6页
Acta Automatica Sinica
基金
国家自然科学基金(60274015)
国家"863"计划(2002AA412420)资助~~
关键词
故障诊断
小波包
时-频分布
图像处理
声信号
Abrasion
Acoustic imaging
Bearings (machine parts)
Image processing
Mathematical models
Wavelet transforms
